Abstract
Disaster resilience has become a key focus of risk reduction efforts, but measuring it remains complex due to differences in hazards, development paths, and data systems. This study modifies the Baseline Resilience Indicators for Communities (BRIC) approach, based on the Disaster Resilience of Place (DROP) framework, to evaluate community resilience in Serbia and highlight regional differences. An initial list of 186 indicators was created from international BRIC studies and resilience research, then tailored to Serbian conditions through contextual review and data checks. Indicators were normalized using min–max scaling (0–1), and indicators with negative orientation were inverted to ensure that higher values indicate greater resilience. Scores for each dimension were calculated as equally weighted averages across six areas: social, economic, social capital, institutional, infrastructural, and environmental. The overall BRIC index was derived as the average of these dimension scores. Z-scores facilitated the classification of resilience levels and the comparison between regions. The results show clear regional disparities: in the complete model, Belgrade has the highest resilience (BRIC = 0.557), while Southern and Eastern Serbia have the lowest (BRIC = 0.414). Patterns across dimensions show that Belgrade excels in social and economic capacity but lags in environmental indicators; Vojvodina has the strongest institutional and infrastructural capacity; and Šumadija and Western Serbia perform best in environmental indicators. Correlation analysis revealed multicollinearity, leading to the removal of 14 redundant indicators and the refinement to a set of 57. After this reduction, regional rankings change, with Vojvodina (BRIC = 0.530) and Šumadija and Western Serbia (BRIC = 0.522) emerging as higher-resilience regions, while Southern and Eastern Serbia remain the least resilient (BRIC = 0.456). The adapted BRIC-DROP model offers a clear, locally relevant tool for mapping resilience and guiding targeted policies in Serbia, enabling region-specific efforts to address structural resilience gaps.
Conclusions
The BRIC method’s theoretical foundation within the DROP framework, along with the careful selection and validation of indicators and the thorough analysis of local communities in Serbia, facilitated the creation of a predictive model for disaster resilience. This model is directly applicable in the Serbian context and methodologically comparable to international studies. Since there is no single universal approach to measuring resilience—due to variations in risk profiles, capacities, institutional setups, and data availability across territories—this study explored resilience dimensions and indicator categories both separately and interactively. The resulting framework captures not only an overall “level of resilience” but also the determinants that influence, shape, or limit resilience outcomes across different locations. The goal was not only to confirm the association between indicators and resilience but also to clarify the nature, direction, and significance of these relationships based on empirical evidence. A key methodological innovation is the application of Pearson correlation screening to identify and remove highly redundant indicators, especially within social and economic dimensions. From this process, 14 redundant indicators were discarded, resulting in a refined Serbian indicator set of 57 across six dimensions. All indicators were normalized to a 0–1 scale through min–max transformation, with negatively oriented indicators inverted (X_adj = 1 − X_norm) so that higher values uniformly indicated greater resilience. Dimension scores were calculated as the average of retained indicators, and the overall BRIC index was derived as an equal-weighted mean of the six dimensions. This approach minimizes multicollinearity and double-counting of related phenomena, enhances the transparency of the composite index, and provides a more stable basis for interpretation, while preserving core information through meaningful retained indicators. Hence, the model maintains international comparability while being adaptable and sensitive to Serbia’s specific context—an essential feature for modern BRIC adaptations.
The revised results after indicator reduction confirm notable regional disparities and show that the “resilience picture” shifts once redundant measures are eliminated. From a policy perspective, the findings clearly indicate that resilience strengthening in Serbia cannot follow a “one-size-fits-all” approach; rather, regionally differentiated interventions are required. In Southern and Eastern Serbia, key priorities include upgrading basic infrastructure, enhancing institutional capacity and emergency management, and encouraging local economic growth to reduce long-term vulnerability and bridge the resilience gap. In Belgrade, efforts should specifically focus on environmental and urban stressors, such as air pollution, urban flooding, and heat risks, to ensure that environmental issues do not undermine socio-economic strengths. Vojvodina, where institutional and infrastructural capacities are strongest in the reduced model, should focus on consolidating these improvements through risk-informed governance, multi-hazard planning, infrastructure maintenance, and integrating land-use with flood and fire risk strategies. In Šumadija and Western Serbia, policies should leverage economic and social advantages while improving infrastructural resilience and service access, especially in dispersed settlements and for response logistics.
Overall, the predictive model and the revised indicator set enhance the evidence base on disaster resilience in Serbia, transforming it from a theoretical idea into a transparent, measurable, and comparable framework. This enables the creation of resilience maps, the identification of critical gaps, the prioritization of investments, and the monitoring of changes over time. The shifts in rankings after reducing the number of indicators underscore the sensitivity of composite indices to their structure and selection, underscoring the importance of methodological transparency for accurate interpretation and policymaking. Future efforts should expand the model to smaller administrative units, such as municipalities and cities, and incorporate repeated measurements over time to monitor resilience dynamics and assess the effectiveness of interventions within Serbia’s integrated disaster risk management system. This study contributes in several ways: by offering a localized BRIC–DROP operationalization suitable for standardized baseline benchmarking; by developing an empirically grounded regional resilience profile to aid prioritization and targeted planning; and by providing a transparent workflow for index construction that can be adapted to other contexts. Future research should integrate baseline capacity indices with scenario-based stress testing across multiple extreme conditions, include process-oriented measures of dynamic adaptive capacity, and expand the assessment to more refined spatial scales and longitudinal studies.
The framework can be shared as a replicable methodological pipeline that includes dimension structure, normalization, aggregation, and reporting logic. However, the specific indicator set and operational definitions should be tailored to local governance structures, hazard scenarios, and data availability. To apply this approach in other countries, one should (i) align candidate indicators with the same conceptual dimensions, (ii) evaluate data comparability and coverage, (iii) test how sensitive the results are to different weighting and reduction methods, and (iv) validate the index against relevant outcomes and stakeholder expectations.
How to cite
Cvetković, V. M., Milenković, D., & Lukić, T. (2026). Measuring Community Disaster Resilience in Serbia Using an Adapted BRIC Framework Grounded in DROP: Index Construction and Regional Disparities. Geosciences, 16(4), 135. https://doi.org/10.3390/geosciences16040135
